Graph-based models to classify effective collaboration in SME networks

Abstract

During the last decades, globalisation of goods’ markets and large disequilibrium amongst labour markets in different countries had a remarkable impact on small and medium enterprises – SMEs, which have been for many years the actual engine of industrial development in Europe. An ‘antidote’ to this crisis is the development of more profitable SME networks in the forms of either ‘clusters’, ‘competitiveness poles’, ‘industrial districts’ or ‘scientific parks’. A more strategic approach is needed, that builds upon existing SME aggregations and explores their main strong and weak points, such as to establish a framework for new innovative networks. The goal of this study is to offer a method to analyse the main features of existing SME networks, in order to offer to the network coordination/management committees some key parameters (KP) to evaluate the network composition and potentially to select networks modifications. The study analyses an archive of industrial networks provided by the European project CODESNET and defines different types of networks and their main KP. A further result of the analysis will be the modelling of each network type in terms of a specific graph. Graphs give a more intuitive representation of the network and an easier comprehension of its organisation. In a second stage, by a probabilistic approach, the authors propose an identification procedure to classify existing and new networks.